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The impact of R&D subsidies on R&D employment composition

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  • Sergio Afcha
  • Jose García-Quevedo

Abstract

In this article, we examine the impact of subsidies granted at national and regional levels on a set of research and development (R&D) employment variables, and we specifically seek to identify the existence of additional effects of these public subsidies on the R&D human resources of firms. We begin by assessing the effects of public funds on private R&D expenditure and on the number of R&D employees, and then focus on the impact of these funds on the composition of human resources engaged in R&D classified by occupation and level of education. The data used are from the Spanish Technological Innovation Panel for the period 2006–2011. To control for selection bias and endogeneity, a combination of nonparametric matching techniques is used. Our results show that R&D subsidies increase the number of R&D employees, but no contemporaneous increase is found in the average level of qualification of R&D staff members in subsidized firms. Nevertheless, in the subsequent years there is a positive effect on the recruitment of PhD holders. The effects of public support are heterogeneous and are dependent on the source of the subsidy and the firms’ characteristics.

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  • Sergio Afcha & Jose García-Quevedo, 2016. "The impact of R&D subsidies on R&D employment composition," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 25(6), pages 955-975.
    • Handle: RePEc:oup:indcch:v:25:y:2016:i:6:p:955-975.
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    2. Modou Mar & Nadine Massard, 2021. "Animate the cluster or subsidize collaborative R&D? A multiple overlapping treatments approach to assess the impacts of the French cluster policy [The impact of R&D subsidies on R&D employment comp," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 30(4), pages 845-867.
    3. Lanahan, Lauren & Joshi, Amol M. & Johnson, Evan, 2021. "Do public R&D subsidies produce jobs? Evidence from the SBIR/STTR program," Research Policy, Elsevier, vol. 50(7).
    4. Li, Jiachen & Jiang, Meiru & Li, Ge, 2024. "Does the new energy vehicles subsidy policy decrease the carbon emissions of the urban transport industry? Evidence from Chinese cities in Yangtze River Delta," Energy, Elsevier, vol. 298(C).
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    6. Afcha, S. & García-Quevedo, J. & Mas-Verdú, F., 2023. "Gaining or losing PhDs: What are the effects on firms' linkages with universities?," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    7. Cao, Qinwei & Qiu, Shunli & Huang, Jian, 2022. "Contradiction and mechanism analysis of science and technology input-output: Evidence from key universities in China," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
    8. Jugend, Daniel & Fiorini, Paula De Camargo & Armellini, Fabiano & Ferrari, Aline Gabriela, 2020. "Public support for innovation: A systematic review of the literature and implications for open innovation," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
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    More about this item

    JEL classification:

    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • H25 - Public Economics - - Taxation, Subsidies, and Revenue - - - Business Taxes and Subsidies
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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